2015/03/03 by John Meehan, Nesime Tatbul, Meehan, John +24
Business, Management and Accounting · Computer Science · #Customer churn and segmentation #Databases (cs.DB) #Digital Platforms and Economics #FOS: Computer and information sciences #Peer-to-Peer Network Technologies #cs.DB
paper · pdf · doi:10.48550/arxiv.1503.01143
openalex publication_date 2015/03/03 · arxiv created 2015/03/10 · arxiv updated 2015/03/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Stream processing addresses the needs of real-time applications. Transaction processing addresses the coordination and safety of short atomic computations. Heretofore, these two modes of operation existed in separate, stove-piped systems. In this work, we attempt to fuse the two computational paradigms in a single system called S-Store. In this way, S-Store can simultaneously accommodate OLTP and streaming applications. We present a simple transaction model for streams that integrates seamlessly with a traditional OLTP system. We chose to build S-Store as an extension of H-Store, an open-source, in-memory, distributed OLTP database system. By implementing S-Store in this way, we can make use of the transaction processing facilities that H-Store already supports, and we can concentrate on the additional implementation features that are needed to support streaming. Similar implementations could be done using other main-memory OLTP platforms. We show that we can actually achieve higher throughput for streaming workloads in S-Store than an equivalent deployment in H-Store alone. We also show how this can be achieved within H-Store with the addition of a modest amount of new functionality. Furthermore, we compare S-Store to two state-of-the-art streaming systems, Spark Streaming and Storm, and show how S-Store matches and sometimes exceeds their performance while providing stronger transactional guarantees.